Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 6W. RSA
- 1Y. RSA
- 6W. RPV
- 1Y. RPV
- 6W. RLL
- 1Y. RLL
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- Ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt
- RSA
- RPV

Propensity Scores Common Support

Model Stats

  • Treatment proportion: 0.118
  • Model Type: elastic_net
  • Accuracy: 0.9022468
  • Params: alpha: 0.1 lambda: 0.0104406

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-72.00 -21.00 -10.95  -4.00  27.55 
Model Type Y: boosting 
RMSE: 16.9718037336247 
Params: nrounds: 50.0
max_depth: 2
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 13.1636472877616 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -1.113 (Std.Error: 5.219)
Trimmed ATE (Yes-No): -0.688 (Std.Error: 5.412)
Upper ATE (Yes-No): -13.009 (Std.Error: 4.87)
Observational differences in treatment 2.388 (Yes-No) 

   treatment  outcome
1:       Yes 23.47162
2:        No 21.08333
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-64.00 -22.69 -10.10  -3.00  22.44 
Model Type Y: boosting 
RMSE: 18.1142617792539 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 13.9961133195629 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): 0.848 (Std.Error: 4.97)
Trimmed ATE (Yes-No): 1.217 (Std.Error: 5.164)
Upper ATE (Yes-No): -9.162 (Std.Error: 7.43)
Observational differences in treatment 3.62 (Yes-No) 

   treatment  outcome
1:       Yes 24.42677
2:        No 20.80704
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-23.631420  -6.000000  -1.516567   1.644172  18.000000 
Model Type Y: boosting 
RMSE: 7.35276696870973 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.14368992329071 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -5.704 (Std.Error: 1.299)
Trimmed ATE (Yes-No): -5.777 (Std.Error: 1.33)
Upper ATE (Yes-No): -3.512 (Std.Error: 3.891)
Observational differences in treatment -1.756 (Yes-No) 

   treatment   outcome
1:       Yes -4.404929
2:        No -2.649229
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-30.098675  -6.000000  -2.018531   1.098470  20.000000 
Model Type Y: boosting 
RMSE: 8.77634284383404 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 5.89994924361389 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -3.668 (Std.Error: 1.587)
Trimmed ATE (Yes-No): -3.567 (Std.Error: 1.644)
Upper ATE (Yes-No): -5.891 (Std.Error: 4.023)
Observational differences in treatment -1.504 (Yes-No) 

   treatment   outcome
1:       Yes -4.114989
2:        No -2.611074
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Sagittal Balance
Distribution:
      0%      25%      50%      75%     100% 
-194.790  -69.015  -30.300   -0.535   89.000 
Model Type Y: boosting 
RMSE: 64.3149261181443 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 53.3308732376719 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -38.537 (Std.Error: 11.145)
Trimmed ATE (Yes-No): -38.992 (Std.Error: 11.491)
Upper ATE (Yes-No): -27.613 (Std.Error: 30.06)
Observational differences in treatment -15.352 (Yes-No) 

   treatment  outcome
1:       Yes 18.15556
2:        No 33.50736
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-237.4700  -67.3100  -30.3250    6.1425   89.3700 
Model Type Y: boosting 
RMSE: 66.1476058846118 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 52.1126535789743 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -40.204 (Std.Error: 12.087)
Trimmed ATE (Yes-No): -39.411 (Std.Error: 12.474)
Upper ATE (Yes-No): -58.604 (Std.Error: 27.071)
Observational differences in treatment -18.507 (Yes-No) 

   treatment  outcome
1:       Yes 19.17207
2:        No 37.67873
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Global Tilt
Distribution:
    0%    25%    50%    75%   100% 
-68.62 -18.13  -6.10   1.85 149.41 
Model Type Y: boosting 
RMSE: 15.3943954382658 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 14.8985523065635 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -10.57 (Std.Error: 4.156)
Trimmed ATE (Yes-No): -10.515 (Std.Error: 4.304)
Upper ATE (Yes-No): -12.046 (Std.Error: 6.527)
Observational differences in treatment -7.037 (Yes-No) 

   treatment  outcome
1:       Yes 18.44622
2:        No 25.48276
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Global Tilt
Distribution:
    0%    25%    50%    75%   100% 
-62.63 -16.41  -6.00   1.00  26.00 
Model Type Y: boosting 
RMSE: 16.7413856052279 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 11.9007612670969 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -14.215 (Std.Error: 4.102)
Trimmed ATE (Yes-No): -14.239 (Std.Error: 4.228)
Upper ATE (Yes-No): -13.629 (Std.Error: 8.963)
Observational differences in treatment -5.197 (Yes-No) 

   treatment  outcome
1:       Yes 20.72767
2:        No 25.92511
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-94.93 -24.00 -10.00   0.00  29.00 
Model Type Y: boosting 
RMSE: 21.6078174163869 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 15.7456421372965 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -3.213 (Std.Error: 3.871)
Trimmed ATE (Yes-No): -2.985 (Std.Error: 3.948)
Upper ATE (Yes-No): -9.637 (Std.Error: 9.858)
Observational differences in treatment -2.083 (Yes-No) 

   treatment   outcome
1:       Yes -51.48541
2:        No -49.40239
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-94.630 -25.000  -8.185   0.000  23.380 
Model Type Y: boosting 
RMSE: 24.6052579105707 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 15.8314219968046 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -13.231 (Std.Error: 6.546)
Trimmed ATE (Yes-No): -13.295 (Std.Error: 6.812)
Upper ATE (Yes-No): -11.471 (Std.Error: 10.798)
Observational differences in treatment 1.336 (Yes-No) 

   treatment   outcome
1:       Yes -47.94133
2:        No -49.27766
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. LGap
Distribution:
       0%       25%       50%       75%      100% 
-96.12340 -24.65500  -9.46750   0.32145  78.92000 
Model Type Y: boosting 
RMSE: 22.7006516458783 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 17.659913109073 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -3.907 (Std.Error: 4.602)
Trimmed ATE (Yes-No): -3.719 (Std.Error: 4.652)
Upper ATE (Yes-No): -9.186 (Std.Error: 9.059)
Observational differences in treatment -3.784 (Yes-No) 

   treatment  outcome
1:       Yes 10.34216
2:        No 14.12575
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. LGap
Distribution:
       0%       25%       50%       75%      100% 
-94.80820 -24.99740  -8.41220   0.13815  22.08000 
Model Type Y: boosting 
RMSE: 26.756721347887 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 15.9783568976971 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -12.42 (Std.Error: 5.56)
Trimmed ATE (Yes-No): -12.36 (Std.Error: 5.707)
Upper ATE (Yes-No): -14.016 (Std.Error: 11.26)
Observational differences in treatment -1.137 (Yes-No) 

   treatment  outcome
1:       Yes 12.61173
2:        No 13.74835
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Pelvic Tilt
Distribution:
     0%     25%     50%     75%    100% 
-36.410  -8.665  -2.420   2.120  14.420 
Model Type Y: boosting 
RMSE: 10.2894672097196 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 7.57514516825294 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -4.353 (Std.Error: 3.411)
Trimmed ATE (Yes-No): -4.186 (Std.Error: 3.494)
Upper ATE (Yes-No): -9.506 (Std.Error: 5.664)
Observational differences in treatment -3.724 (Yes-No) 

   treatment  outcome
1:       Yes 18.29917
2:        No 22.02346
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
     0%     25%     50%     75%    100% 
-26.620  -7.000  -2.015   2.000  23.000 
Model Type Y: boosting 
RMSE: 9.54009859832513 
Params: nrounds: 50.0
max_depth: 3
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.92609217711346 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -7.377 (Std.Error: 2.758)
Trimmed ATE (Yes-No): -7.509 (Std.Error: 2.84)
Upper ATE (Yes-No): -3.841 (Std.Error: 5.109)
Observational differences in treatment -3.064 (Yes-No) 

   treatment  outcome
1:       Yes 19.68833
2:        No 22.75278
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RSA
Distribution:
      0%      25%      50%      75%     100% 
-67.5592 -18.1156  -6.3156   2.0000  76.5028 
Model Type Y: boosting 
RMSE: 14.3394495354326 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 13.3748279717938 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -11.417 (Std.Error: 3.623)
Trimmed ATE (Yes-No): -11.445 (Std.Error: 3.766)
Upper ATE (Yes-No): -10.676 (Std.Error: 5.193)
Observational differences in treatment -5.57 (Yes-No) 

   treatment   outcome
1:       Yes  7.430605
2:        No 13.000612
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RSA
Distribution:
      0%      25%      50%      75%     100% 
-62.4716 -16.5396  -6.0948   1.0000  25.0400 
Model Type Y: boosting 
RMSE: 16.0339449902985 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 11.7438774501608 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -13.425 (Std.Error: 3.683)
Trimmed ATE (Yes-No): -13.393 (Std.Error: 3.835)
Upper ATE (Yes-No): -14.198 (Std.Error: 9.29)
Observational differences in treatment -2.735 (Yes-No) 

   treatment  outcome
1:       Yes 10.84495
2:        No 13.57991
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RPV
Distribution:
        0%        25%        50%        75%       100% 
-85.555100  -2.274800   2.157300   8.233225  35.503900 
Model Type Y: boosting 
RMSE: 10.0766864059638 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 8.60182698927107 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): 5.125 (Std.Error: 3.707)
Trimmed ATE (Yes-No): 5.105 (Std.Error: 3.823)
Upper ATE (Yes-No): 5.735 (Std.Error: 4.876)
Observational differences in treatment 3.999 (Yes-No) 

   treatment   outcome
1:       Yes -4.583197
2:        No -8.582035
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RPV
Distribution:
        0%        25%        50%        75%       100% 
-22.180000  -1.385350   2.384400   6.666725  26.634600 
Model Type Y: boosting 
RMSE: 9.76399387842464 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.65912295435667 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 8.114 (Std.Error: 2.631)
Trimmed ATE (Yes-No): 8.272 (Std.Error: 2.694)
Upper ATE (Yes-No): 3.396 (Std.Error: 5.359)
Observational differences in treatment 1.018 (Yes-No) 

   treatment   outcome
1:       Yes -7.435010
2:        No -8.453427
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RLL
Distribution:
      0%      25%      50%      75%     100% 
-87.1818  -0.2544   9.5188  24.8100  96.3002 
Model Type Y: boosting 
RMSE: 18.7332051982061 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 17.043979793129 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): 3.334 (Std.Error: 4.017)
Trimmed ATE (Yes-No): 3.05 (Std.Error: 4.215)
Upper ATE (Yes-No): 11.282 (Std.Error: 9.55)
Observational differences in treatment 3.823 (Yes-No) 

   treatment   outcome
1:       Yes -11.11809
2:        No -14.94157
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RLL
Distribution:
       0%       25%       50%       75%      100% 
-22.58000  -0.36225   8.40660  25.03865  94.83460 
Model Type Y: boosting 
RMSE: 24.8837318525668 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.9161259210216 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): 11.689 (Std.Error: 7.186)
Trimmed ATE (Yes-No): 11.425 (Std.Error: 7.335)
Upper ATE (Yes-No): 18.746 (Std.Error: 14.745)
Observational differences in treatment 1.519 (Yes-No) 

   treatment   outcome
1:       Yes -13.19885
2:        No -14.71802
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'